Agentic RAG

What is Agentic RAG?

Agentic Retrieval-Augmented Generation (RAG) is an advanced AI framework that integrates intelligent agents into the traditional RAG system. Traditional RAG combines large language models (LLMs) with external knowledge sources to enhance the accuracy of responses by providing additional context to the LLM. Agentic RAG builds on this foundation by enabling AI agents to autonomously analyze queries, make strategic decisions, and perform multi-step reasoning. This approach allows systems to manage complex tasks across diverse datasets, bringing a dynamic and flexible approach to information retrieval.

How is Agentic RAG Used?

Agentic RAG is primarily used to improve the efficiency and accuracy of complex information retrieval tasks. By employing AI agents, it goes beyond static, rule-based systems and introduces intelligent, adaptive frameworks capable of real-time planning and execution. These agents can leverage multiple data sources, external tools, and APIs to retrieve, evaluate, and synthesize information, thereby providing more comprehensive and context-aware responses.

Use Cases

Real-time Adaptive Query Responses

Agentic RAG ensures that both employees and customers receive accurate information promptly, enhancing productivity through efficient data management.

Automated Support Systems

By providing quick and precise answers to inquiries, agentic RAG reduces the workload on human agents, resulting in improved efficiency and response times.

Internal Knowledge Management

Agentic RAG streamlines access to crucial information within organizations, aiding employees in making informed decisions swiftly and efficiently.

Research and Innovation Support

The system assists in synthesizing and presenting relevant data for strategic initiatives, supporting innovation and research efforts.

Key Features of Agentic RAG

• Adaptive Reasoning: Utilizes a reasoner to interpret user intent, develop strategic plans for information retrieval, and evaluate data source reliability.
• Collaborative Agent Network: Functions as a network of specialized agents, each skilled in handling extensive and diverse datasets.
• Dynamic Planning and Execution: Capable of real-time planning and optimization of query processes, allowing for more effective handling of complex queries.
• Intelligent Quality Control: Agents not only retrieve data but also evaluate and verify information to ensure accurate and reliable outputs.
• External Tool Integration: Allows agents to utilize a variety of external tools and resources, enhancing information gathering and processing capabilities.

Implementation Strategies

Language Models with Function Calling

Agentic RAG systems can be built using language models with function calling capabilities. This approach allows the models to interact with predefined tools, enabling them to access and cite web resources, execute code, and more.

Agent Frameworks

Frameworks like FlowHunt, DSPy, LangChain, and CrewAI provide pre-built templates and tools to simplify the construction of agentic RAG systems. These frameworks facilitate the integration of multi-agent systems and external resources, enhancing the system’s adaptability and efficiency.

Prompt for document grading

We need to give to agent a tool, which will grade the document found in the indexed Documents. Here is example prompt to classify found document and deciding if the document answer the question of user. Based on this decission can Agent rewrite his search prompt and search again.

You are a grader assessing relevance of a retrieved document to a user question.
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Retrieved document:
{context}
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User question: {question}
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If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant.
Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.

Future Trends

Agentic RAG continues to evolve with advancements in AI technologies. Trends include multi-modal retrieval, cross-lingual capabilities, and enhanced natural language processing, which promise to expand the applicability and effectiveness of agentic RAG systems across various industries.

In summary, agentic RAG represents a leap forward in AI-driven information retrieval, offering a sophisticated approach to managing complex queries and enhancing decision-making processes. Its ability to adapt, reason, and utilize external knowledge positions it as a powerful tool for organizations dealing with large-scale, dynamic information environments.

Research on Agentic RAG

1. RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards
   Published: 2024-10-17
   This paper discusses the optimization of Retrieval-Augmented Generation (RAG) systems to reduce hallucinations in Large Language Models (LLMs) by employing a Differentiable Data Rewards (DDR) method. The study highlights the limitations of traditional supervised fine-tuning (SFT) methods which may cause RAG modules to overfit and neglect the varying data preferences among agents. The DDR method enhances RAG systems by aligning data preferences and optimizing agents to produce better outputs, ultimately improving RAG system performance. The experiments show DDR’s significant effectiveness over SFT, especially for smaller-scale LLMs dependent on retrieved knowledge. The research also demonstrates DDR’s superior ability to align data preferences between RAG modules, enhancing the generation module’s efficacy in information extraction and mitigating conflicts. Read more.
2. A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph
   Published: 2024-09-13
   This study explores enhancing knowledge-based QA systems by implementing an advanced RAG system using Graph technology, overcoming limitations of existing models. The research addresses the shortcomings such as accuracy degradation and inability to incorporate real-time data in traditional RAG systems. By utilizing LangGraph, the study enhances the reliability and synthesis of retrieved data for more accurate responses. The paper provides detailed implementation steps and guidelines, making it a practical resource for deploying advanced RAG systems in corporate environments. This approach is aimed at improving the contextual understanding and reducing biases in RAG outputs.
3. Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models
   Published: 2024-08-12
   The paper presents a case study on optimizing RAG techniques for offline PDF chatbots in the automotive industry, focusing on deploying LLMs in low-performance local settings. The study addresses the challenges of processing complex industry-specific documents and enhancing information retrieval and generation capabilities. It showcases the successful application of optimized RAG techniques in creating efficient and reliable chatbots for industrial environments, highlighting the potential for improving information management in production settings. The findings suggest significant improvements in chatbot performance and user satisfaction through tailored RAG implementations.